However, due to limited data resources from downstream tasks and the extremely large capacity of pre-trained models, aggressive fine-tuning often causes the adapted model to overfit the data of downstream tasks and forget the knowledge of the pre-trained model.
Most current license plate (LP) detection and recognition approaches are evaluated on a small and usually unrepresentative dataset since there are no publicly available large diverse datasets.
We perform a thorough ablation study to evaluate our approach on a suite of challenging maze tasks, demonstrating significant advantages from the proposed framework over baselines that lack world graph knowledge in terms of performance and efficiency.
This dataset is composed of 9, 053 road damage images captured with a smartphone installed on a car, with 15, 435 instances of road surface damage included in these road images.
To our knowledge, this is the first application of a fully convolutional neural network architecture for pixel-wise labeling in cardiac magnetic resonance imaging.
The second one (DTHB) is a multi-year effort to express the linguistic features of the Hebrew bible in a text database, which is still growing in detail and sophistication.
We present an approach to map utterances in conversation to logical forms, which will be executed on a large-scale knowledge base.